Dataset statistics
| Number of variables | 16 |
|---|---|
| Number of observations | 2157 |
| Missing cells | 0 |
| Missing cells (%) | 0.0% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 255.1 KiB |
| Average record size in memory | 121.1 B |
Variable types
| DateTime | 1 |
|---|---|
| Categorical | 5 |
| Numeric | 10 |
username has constant value "elonmusk" | Constant |
cashtags has constant value "0" | Constant |
tweet has a high cardinality: 2157 distinct values | High cardinality |
video is highly correlated with photos | High correlation |
photos is highly correlated with video | High correlation |
replies_count is highly correlated with retweets_count and 1 other fields | High correlation |
retweets_count is highly correlated with replies_count and 1 other fields | High correlation |
likes_count is highly correlated with replies_count and 1 other fields | High correlation |
video is highly correlated with photos and 1 other fields | High correlation |
photos is highly correlated with video | High correlation |
replies_count is highly correlated with retweets_count and 2 other fields | High correlation |
retweets_count is highly correlated with replies_count and 2 other fields | High correlation |
likes_count is highly correlated with video and 3 other fields | High correlation |
number of tweets is highly correlated with replies_count and 2 other fields | High correlation |
video is highly correlated with photos | High correlation |
photos is highly correlated with video | High correlation |
replies_count is highly correlated with retweets_count and 1 other fields | High correlation |
retweets_count is highly correlated with replies_count and 1 other fields | High correlation |
likes_count is highly correlated with replies_count and 1 other fields | High correlation |
replies_count is highly correlated with retweets_count and 1 other fields | High correlation |
video is highly correlated with photos | High correlation |
retweets_count is highly correlated with replies_count and 1 other fields | High correlation |
photos is highly correlated with video | High correlation |
bins is highly correlated with percent change | High correlation |
likes_count is highly correlated with replies_count and 1 other fields | High correlation |
percent change is highly correlated with bins | High correlation |
hashtags is highly correlated with cashtags and 1 other fields | High correlation |
cashtags is highly correlated with hashtags and 2 other fields | High correlation |
bins is highly correlated with cashtags and 1 other fields | High correlation |
username is highly correlated with hashtags and 2 other fields | High correlation |
tweet is uniformly distributed | Uniform |
date has unique values | Unique |
tweet has unique values | Unique |
mentions has 1944 (90.1%) zeros | Zeros |
video has 1668 (77.3%) zeros | Zeros |
photos has 1706 (79.1%) zeros | Zeros |
urls has 1609 (74.6%) zeros | Zeros |
Reproduction
| Analysis started | 2021-09-27 19:02:25.376573 |
|---|---|
| Analysis finished | 2021-09-27 19:02:43.719925 |
| Duration | 18.34 seconds |
| Software version | pandas-profiling v3.0.0 |
| Download configuration | config.json |
| Distinct | 2157 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 17.0 KiB |
| Minimum | 2016-08-23 16:00:00 |
|---|---|
| Maximum | 2021-07-20 09:30:00 |
Histogram with fixed size bins (bins=50)
| Distinct | 2157 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 17.0 KiB |
| Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today | 1 |
|---|---|
| @KMastersBarnes @SpaceX Yeah, most reflights ever! @SciGuySpace Yeah. There was also an early engine shutdown on ascent, but it didn’t affect orbit insertion. Shows value of having 9 engines! Thorough investigation needed before next mission. @EvaFoxU 🤣🤣 Because polygon doesn’t rhyme @annerajb Yeah @PPathole Awe tow co wrecked Abysmal autocorrect might be the #1 reason people don’t fear AI Fear the memesphere @nichegamer Wild times … @j_potoski @AdrianaGalayda @PPathole @1971capital @MLevitt_NP2013 Exactly @nichegamer Seems to be happening a lot @1971capital @MLevitt_NP2013 Very sensible. Knows how to handle exponential functions in reality. @RiganoESQ @DiderRaoult Whether Z-pak works in this situation or not, it’s a kickass med for many maladies https://t.co/UM2TqYpZQZ | 1 |
| @alvianchoiri @utkarshzaveri @fael097 Yes https://t.co/lzh1uj8QCy @MartianDays Physics is the law, everything else is a recommendation SN3 https://t.co/bM1wzzd4Zg | 1 |
| @flcnhvy @Tesla Giga New York will reopen for ventilator production as soon as humanly possible. We will do anything in our power to help the citizens of New York. @enscand @PPathole @flcnhvy @Tesla Something weird happened at CDC yesterday. They changed the graph to include “estimated illness onset date”. This is a significantly less rigorous standard. https://t.co/Dz2Nio5ddi @PPathole @flcnhvy @Tesla C19 testing in the US over the past week has grown much faster than C19 positive cases. I think we may have passed the inflection point for US cases (excluding NY) already. @PPathole @flcnhvy @Tesla Yes. Is there more? Should be a lot of data by now. @flcnhvy @Tesla Making good progress. We will do whatever is needed to help in these difficult times. | 1 |
| This meme proves it https://t.co/3CHAzxv6dj It’s ducked! @SamTalksTesla @MikeBloomberg True. @MikeBloomberg, this is accurate. You believe in journalistic integrity, but if something isn’t done, this will continue. @steezyysosa @TesLatino @flcnhvy @thirdrowtesla @MikeBloomberg @Twitter Have let @twitter know @flcnhvy @thirdrowtesla This is messed up @MikeBloomberg | 1 |
| Other values (2152) |
Length
| Max length | 6926 |
|---|---|
| Median length | 274 |
| Mean length | 475.8701901 |
| Min length | 1 |
Characters and Unicode
| Total characters | 1026452 |
|---|---|
| Distinct characters | 343 |
| Distinct categories | 18 ? |
| Distinct scripts | 7 ? |
| Distinct blocks | 16 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 2157 ? |
|---|---|
| Unique (%) | 100.0% |
Sample
| 1st row | Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today |
|---|---|
| 2nd row | @Kotaku one of my favorite games as a kid @BelovedRevol Making progress. Maybe something to announce in a few months. Have played all prior Deus Ex. Not this one yet. |
| 3rd row | Thanks for the longstanding faith in SpaceX. We very much look forward to doing this milestone flight with you. https://t.co/U2UFez0OhY |
| 4th row | @Lockyep Not allowed, according to HK regulations. Happy to do it if regs change. We need to do one more minor rev on 8.0 and then will go to wide release in a few weeks Writing post now with details. Will publish on Tesla website later today. Major improvements to Autopilot coming with V8.0 and 8.1 software (std OTA update) primarily through advanced processing of radar signals @newscientist uh oh |
| 5th row | Loss of Falcon vehicle today during propellant fill operation. Originated around upper stage oxygen tank. Cause still unknown. More soon. |
Common Values
| Value | Count | Frequency (%) |
| Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today | 1 | < 0.1% |
| @KMastersBarnes @SpaceX Yeah, most reflights ever! @SciGuySpace Yeah. There was also an early engine shutdown on ascent, but it didn’t affect orbit insertion. Shows value of having 9 engines! Thorough investigation needed before next mission. @EvaFoxU 🤣🤣 Because polygon doesn’t rhyme @annerajb Yeah @PPathole Awe tow co wrecked Abysmal autocorrect might be the #1 reason people don’t fear AI Fear the memesphere @nichegamer Wild times … @j_potoski @AdrianaGalayda @PPathole @1971capital @MLevitt_NP2013 Exactly @nichegamer Seems to be happening a lot @1971capital @MLevitt_NP2013 Very sensible. Knows how to handle exponential functions in reality. @RiganoESQ @DiderRaoult Whether Z-pak works in this situation or not, it’s a kickass med for many maladies https://t.co/UM2TqYpZQZ | 1 | < 0.1% |
| @alvianchoiri @utkarshzaveri @fael097 Yes https://t.co/lzh1uj8QCy @MartianDays Physics is the law, everything else is a recommendation SN3 https://t.co/bM1wzzd4Zg | 1 | < 0.1% |
| @flcnhvy @Tesla Giga New York will reopen for ventilator production as soon as humanly possible. We will do anything in our power to help the citizens of New York. @enscand @PPathole @flcnhvy @Tesla Something weird happened at CDC yesterday. They changed the graph to include “estimated illness onset date”. This is a significantly less rigorous standard. https://t.co/Dz2Nio5ddi @PPathole @flcnhvy @Tesla C19 testing in the US over the past week has grown much faster than C19 positive cases. I think we may have passed the inflection point for US cases (excluding NY) already. @PPathole @flcnhvy @Tesla Yes. Is there more? Should be a lot of data by now. @flcnhvy @Tesla Making good progress. We will do whatever is needed to help in these difficult times. | 1 | < 0.1% |
| This meme proves it https://t.co/3CHAzxv6dj It’s ducked! @SamTalksTesla @MikeBloomberg True. @MikeBloomberg, this is accurate. You believe in journalistic integrity, but if something isn’t done, this will continue. @steezyysosa @TesLatino @flcnhvy @thirdrowtesla @MikeBloomberg @Twitter Have let @twitter know @flcnhvy @thirdrowtesla This is messed up @MikeBloomberg | 1 | < 0.1% |
| @JBNielsen1985 @stephenpallotta @ajtourville @Teslarati Both @InSpaceXItrust @Kristennetten @thirdrowtesla Could maybe tap the condensation for water too. Seems odd that HVAC systems make pure, fresh water & just dump it on the ground. @SteveHamel16 @JordanWells33 @hereforthecom19 @ScottWapnerCNBC Thanks Tesla China team, China Customs Authority & LAX customs for acting so swiftly @SteveHamel16 @JordanWells33 @hereforthecom19 @ScottWapnerCNBC Yup, China had an oversupply, so we bought 1255 FDA-approved ResMed, Philips & Medtronic ventilators on Friday night & airshipped them to LA. If you want a free ventilator installed, please let us know! @Kristennetten @thirdrowtesla Yeah, pretty much. House could talk to car & know when you’re expected home, so temp & humidity would be perfect just as you arrive. No wasted energy. @romn8tr @stephenpallotta @ajtourville @Teslarati This is a way bigger deal than most people realize @stephenpallotta @ajtourville @Teslarati Sure would love to do home hvac that’s quiet & efficient, with humidity control & HEPA filter @stephenpallotta @ajtourville @Teslarati Yes. PCB design techniques applied to create a heat exchanger that is physically impossible by normal means. Heat pump also has a local heating loop to spool up fast & extend usable temperature range. Octavalve is pretty special too. Team did great work. No credit to me. @ajtourville @Teslarati The heat pump and rear body castings are a step beyond @Teslarati Model Y heat pump is some of the best engineering I’ve seen in a while. Team did next-level work. @justpaulinelol @engineers_feed 🤣🤣 @BocachicaMaria1 Will do @teslaownersSV @RenataKonkoly @sdunbabin @jonkay @Quillette Most likely, imo, we will see a significant reduction in the confirmed C19 case growth rate this week. Follow chart at bottom of this CDC page: https://t.co/vZ3PbhQihG @RenataKonkoly @sdunbabin @jonkay @Quillette Exactly @jonkay @Quillette According to the Italian govt, only 12% of deaths are actually due to C19. This is a significant policy difference in Italy vs most other countries. @engineers_feed Engineering ftw @flyLAXairport Much appreciated | 1 | < 0.1% |
| @PPathole @BBCScienceNews Have you seen more data on HCQ & Z-Pak? Hard to find. @BBCScienceNews Worrisome @thirdrowtesla Yup @JohnnaCrider1 @thirdrowtesla We’ll try to get & deliver as many as possible. N95 masks are a pain to wear btw. Less onerous masks are better most of the time. @Kristennetten @thirdrowtesla @SalehCU Yeah. We have a mask shipment stuck at LAX. Hopefully freed up soon. @engineers_feed Nice @JohnCleese This is great advice @uwdrwaldorf @Tesla @omead_a @UWMedicine Thanks for taking delivery in your garage! Let us know if there’s anything else you need. @justpaulinelol @EvaFoxU @Tesla We expect to have over ~1200 to distribute this week. Getting them delivered, installed & operating is the harder part. @EvaFoxU @Tesla Supply chain logistics — getting masks & other PPE to the right places in time — is the main issue we’re hearing from ER physicians @NativeCACV @Tesla @omead_a You’re most welcome. We’re working on getting other types of PPE too. Ventilators should arrive within a few days. | 1 | < 0.1% |
| @benikbeno @CDCgov Panic is always dumb @Guruleaks1 @benikbeno Italy: “On re-evaluation by the National Institute of Health, only 12 per cent of death certificates have shown a direct causality from coronavirus.” @CDCgov Close contact family gatherings that mix young kids, who have almost no risk of C19 death, with grandparents who have high risk (especially those with prior lung damage), are one of the most powerful mortality vectors | 1 | < 0.1% |
| @Jennerator211 We have N95 masks & getting PAPRs. Will have our team reach out. | 1 | < 0.1% |
| Just had a long engineering discussion with Medtronic about state-of-the-art ventilators. Very impressive team! @SamTalksTesla @flcnhvy @thirdrowtesla @NateSilver538 No problem :) @SamTalksTesla @flcnhvy @thirdrowtesla @NateSilver538 Sigmoid (S-curve) is how all physical and mental viruses behave, regardless of containment. Containment reduces asymptote of S-curve. At this point, we have strict containment in US/Europe & should expect similarly reduced asymptote to China. @flcnhvy @thirdrowtesla @NateSilver538 Sigmoidal for China, followed by sigmoidal for rest of world @Lauren62515251 @PPathole @NateSilver538 Fair point :) @RiccitelliDylan @ColdBuschLights @PPathole @NateSilver538 Exactly, both would be false positive. Also, dying with C19 is different from dying because of C19. Vast majority who died had other illnesses too. https://t.co/YKMW54q2kL @PPathole @NateSilver538 Up to 80% false positive @PPathole @NateSilver538 Sure, although ventilator companies definitely know how to make ventilators. Just a spike in demand right now. Also, using CPAP machines for less severe cases & using one ventilator for several patients seem like good moves to meet short-term demand. @NateSilver538 Do you have data on the false positive rate? I’ve heard a wide range of numbers. CDC numbers are almost an order of magnitude lower between high fidelity data (onset date known) and “presumed positive”. https://t.co/vZ3PbhQihG @NateSilver538 Important consideration | 1 | < 0.1% |
| Other values (2147) | 2147 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| to | 3519 | 2.3% |
| the | 3178 | 2.1% |
| is | 2759 | 1.8% |
| a | 2718 | 1.8% |
| of | 2336 | 1.5% |
| amp | 1945 | 1.3% |
| in | 1829 | 1.2% |
| for | 1593 | 1.0% |
| tesla | 1585 | 1.0% |
| will | 1328 | 0.9% |
| Other values (18820) | 130347 |
Most occurring characters
| Value | Count | Frequency (%) |
| 152955 | ||
| e | 85937 | 8.4% |
| a | 65732 | 6.4% |
| t | 65477 | 6.4% |
| o | 58861 | 5.7% |
| i | 51540 | 5.0% |
| s | 49688 | 4.8% |
| r | 49202 | 4.8% |
| n | 48414 | 4.7% |
| l | 39327 | 3.8% |
| Other values (333) | 359319 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 747582 | |
| Space Separator | 152955 | 14.9% |
| Uppercase Letter | 56415 | 5.5% |
| Other Punctuation | 46165 | 4.5% |
| Decimal Number | 14404 | 1.4% |
| Final Punctuation | 2329 | 0.2% |
| Connector Punctuation | 2152 | 0.2% |
| Other Symbol | 1459 | 0.1% |
| Dash Punctuation | 870 | 0.1% |
| Close Punctuation | 575 | 0.1% |
| Other values (8) | 1546 | 0.2% |
Most frequent character per category
Other Symbol
| Value | Count | Frequency (%) |
| 🤣 | 424 | |
| ♥ | 122 | 8.4% |
| 🔥 | 58 | 4.0% |
| 😀 | 48 | 3.3% |
| 🖤 | 41 | 2.8% |
| 🚀 | 38 | 2.6% |
| 😉 | 33 | 2.3% |
| 💕 | 29 | 2.0% |
| 👍 | 24 | 1.6% |
| 🚘 | 17 | 1.2% |
| Other values (192) | 625 |
Lowercase Letter
| Value | Count | Frequency (%) |
| e | 85937 | |
| a | 65732 | 8.8% |
| t | 65477 | 8.8% |
| o | 58861 | 7.9% |
| i | 51540 | 6.9% |
| s | 49688 | 6.6% |
| r | 49202 | 6.6% |
| n | 48414 | 6.5% |
| l | 39327 | 5.3% |
| h | 29311 | 3.9% |
| Other values (41) | 204093 |
Uppercase Letter
| Value | Count | Frequency (%) |
| S | 6017 | 10.7% |
| T | 5996 | 10.6% |
| A | 3718 | 6.6% |
| M | 3271 | 5.8% |
| I | 2986 | 5.3% |
| C | 2771 | 4.9% |
| E | 2603 | 4.6% |
| W | 2485 | 4.4% |
| P | 2411 | 4.3% |
| B | 2129 | 3.8% |
| Other values (18) | 22028 |
Other Punctuation
| Value | Count | Frequency (%) |
| @ | 17453 | |
| . | 9613 | |
| , | 5422 | 11.7% |
| / | 4858 | 10.5% |
| ; | 2039 | 4.4% |
| & | 2034 | 4.4% |
| : | 1618 | 3.5% |
| ! | 1483 | 3.2% |
| ? | 394 | 0.9% |
| … | 350 | 0.8% |
| Other values (5) | 901 | 2.0% |
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 2740 | |
| 1 | 2412 | |
| 2 | 1812 | |
| 3 | 1766 | |
| 5 | 1179 | |
| 4 | 1003 | 7.0% |
| 8 | 938 | 6.5% |
| 9 | 936 | 6.5% |
| 7 | 915 | 6.4% |
| 6 | 703 | 4.9% |
Math Symbol
| Value | Count | Frequency (%) |
| ~ | 273 | |
| + | 97 | 25.0% |
| = | 12 | 3.1% |
| ∩ | 2 | 0.5% |
| ≥ | 2 | 0.5% |
| √ | 1 | 0.3% |
| ∆ | 1 | 0.3% |
Other Letter
| Value | Count | Frequency (%) |
| 中 | 1 | |
| 南 | 1 | |
| 海 | 1 | |
| 紫 | 1 | |
| 光 | 1 | |
| 阁 | 1 | |
| 𖨆 | 1 |
Open Punctuation
| Value | Count | Frequency (%) |
| ( | 514 | |
| [ | 7 | 1.3% |
| { | 1 | 0.2% |
Close Punctuation
| Value | Count | Frequency (%) |
| ) | 567 | |
| ] | 6 | 1.0% |
| } | 2 | 0.3% |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 798 | |
| — | 47 | 5.4% |
| – | 25 | 2.9% |
Currency Symbol
| Value | Count | Frequency (%) |
| $ | 166 | |
| € | 1 | 0.6% |
| £ | 1 | 0.6% |
Initial Punctuation
| Value | Count | Frequency (%) |
| “ | 239 | |
| ‘ | 7 | 2.8% |
Final Punctuation
| Value | Count | Frequency (%) |
| ’ | 2087 | |
| ” | 242 | 10.4% |
Format
| Value | Count | Frequency (%) |
| | 19 | |
| | 1 | 5.0% |
Modifier Symbol
| Value | Count | Frequency (%) |
| ^ | 10 | |
| 🏻 | 1 | 9.1% |
Space Separator
| Value | Count | Frequency (%) |
| 152955 |
Connector Punctuation
| Value | Count | Frequency (%) |
| _ | 2152 |
Nonspacing Mark
| Value | Count | Frequency (%) |
| ️ | 184 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 803951 | |
| Common | 222247 | 21.7% |
| Inherited | 203 | < 0.1% |
| Cyrillic | 39 | < 0.1% |
| Han | 6 | < 0.1% |
| Greek | 5 | < 0.1% |
| Bamum | 1 | < 0.1% |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 152955 | ||
| @ | 17453 | 7.9% |
| . | 9613 | 4.3% |
| , | 5422 | 2.4% |
| / | 4858 | 2.2% |
| 0 | 2740 | 1.2% |
| 1 | 2412 | 1.1% |
| _ | 2152 | 1.0% |
| ’ | 2087 | 0.9% |
| ; | 2039 | 0.9% |
| Other values (246) | 20516 | 9.2% |
Latin
| Value | Count | Frequency (%) |
| e | 85937 | 10.7% |
| a | 65732 | 8.2% |
| t | 65477 | 8.1% |
| o | 58861 | 7.3% |
| i | 51540 | 6.4% |
| s | 49688 | 6.2% |
| r | 49202 | 6.1% |
| n | 48414 | 6.0% |
| l | 39327 | 4.9% |
| h | 29311 | 3.6% |
| Other values (48) | 260462 |
Cyrillic
| Value | Count | Frequency (%) |
| о | 9 | |
| в | 4 | |
| К | 3 | 7.7% |
| р | 3 | 7.7% |
| л | 3 | 7.7% |
| д | 2 | 5.1% |
| к | 2 | 5.1% |
| ё | 2 | 5.1% |
| и | 2 | 5.1% |
| а | 1 | 2.6% |
| Other values (8) | 8 |
Han
| Value | Count | Frequency (%) |
| 中 | 1 | |
| 南 | 1 | |
| 海 | 1 | |
| 紫 | 1 | |
| 光 | 1 | |
| 阁 | 1 |
Inherited
| Value | Count | Frequency (%) |
| ️ | 184 | |
| | 19 | 9.4% |
Greek
| Value | Count | Frequency (%) |
| Δ | 4 | |
| θ | 1 | 20.0% |
Bamum
| Value | Count | Frequency (%) |
| 𖨆 | 1 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 1021709 | |
| Punctuation | 3017 | 0.3% |
| None | 1045 | 0.1% |
| VS | 184 | < 0.1% |
| Emoticons | 162 | < 0.1% |
| Misc Symbols | 161 | < 0.1% |
| Enclosed Alphanum Sup | 70 | < 0.1% |
| Cyrillic | 39 | < 0.1% |
| Dingbats | 25 | < 0.1% |
| Latin 1 Sup | 19 | < 0.1% |
| Other values (6) | 21 | < 0.1% |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 152955 | ||
| e | 85937 | 8.4% |
| a | 65732 | 6.4% |
| t | 65477 | 6.4% |
| o | 58861 | 5.8% |
| i | 51540 | 5.0% |
| s | 49688 | 4.9% |
| r | 49202 | 4.8% |
| n | 48414 | 4.7% |
| l | 39327 | 3.8% |
| Other values (80) | 354576 |
Punctuation
| Value | Count | Frequency (%) |
| ’ | 2087 | |
| … | 350 | 11.6% |
| ” | 242 | 8.0% |
| “ | 239 | 7.9% |
| — | 47 | 1.6% |
| – | 25 | 0.8% |
| | 19 | 0.6% |
| ‘ | 7 | 0.2% |
| | 1 | < 0.1% |
Dingbats
| Value | Count | Frequency (%) |
| ❤ | 16 | |
| ✌ | 6 | 24.0% |
| ✨ | 3 | 12.0% |
VS
| Value | Count | Frequency (%) |
| ️ | 184 |
Emoticons
| Value | Count | Frequency (%) |
| 😀 | 48 | |
| 😉 | 33 | |
| 😢 | 9 | 5.6% |
| 😅 | 8 | 4.9% |
| 😍 | 8 | 4.9% |
| 😎 | 6 | 3.7% |
| 😮 | 5 | 3.1% |
| 😜 | 4 | 2.5% |
| 🙏 | 4 | 2.5% |
| 😔 | 3 | 1.9% |
| Other values (17) | 34 |
None
| Value | Count | Frequency (%) |
| 🤣 | 424 | |
| 🔥 | 58 | 5.6% |
| 🖤 | 41 | 3.9% |
| 🚀 | 38 | 3.6% |
| 💕 | 29 | 2.8% |
| 👍 | 24 | 2.3% |
| 🚘 | 17 | 1.6% |
| 💨 | 17 | 1.6% |
| 💫 | 16 | 1.5% |
| 🛸 | 16 | 1.5% |
| Other values (136) | 365 |
Latin 1 Sup
| Value | Count | Frequency (%) |
| é | 7 | |
| ü | 6 | |
| ö | 4 | |
| £ | 1 | 5.3% |
| ä | 1 | 5.3% |
Enclosed Alphanum Sup
| Value | Count | Frequency (%) |
| 🇳 | 11 | |
| 🇴 | 9 | |
| 🇺 | 7 | |
| 🇮 | 7 | |
| 🇸 | 7 | |
| 🇦 | 5 | |
| 🇪 | 5 | |
| 🇯 | 4 | 5.7% |
| 🇵 | 4 | 5.7% |
| 🇩 | 4 | 5.7% |
| Other values (4) | 7 |
Misc Symbols
| Value | Count | Frequency (%) |
| ♥ | 122 | |
| ☺ | 8 | 5.0% |
| ♂ | 8 | 5.0% |
| ♀ | 6 | 3.7% |
| ⚡ | 6 | 3.7% |
| ☠ | 2 | 1.2% |
| ⚔ | 2 | 1.2% |
| ⛺ | 1 | 0.6% |
| ⚾ | 1 | 0.6% |
| ⛄ | 1 | 0.6% |
| Other values (4) | 4 | 2.5% |
Currency Symbols
| Value | Count | Frequency (%) |
| € | 1 |
CJK
| Value | Count | Frequency (%) |
| 中 | 1 | |
| 南 | 1 | |
| 海 | 1 | |
| 紫 | 1 | |
| 光 | 1 | |
| 阁 | 1 |
Latin Ext A
| Value | Count | Frequency (%) |
| ō | 2 | |
| ē | 1 |
Math Operators
| Value | Count | Frequency (%) |
| ∩ | 2 | |
| ≥ | 2 | |
| √ | 1 | |
| ∆ | 1 |
Letterlike Symbols
| Value | Count | Frequency (%) |
| ™ | 2 | |
| ℏ | 2 |
Cyrillic
| Value | Count | Frequency (%) |
| о | 9 | |
| в | 4 | |
| К | 3 | 7.7% |
| р | 3 | 7.7% |
| л | 3 | 7.7% |
| д | 2 | 5.1% |
| к | 2 | 5.1% |
| ё | 2 | 5.1% |
| и | 2 | 5.1% |
| а | 1 | 2.6% |
| Other values (8) | 8 |
Bamum Sup
| Value | Count | Frequency (%) |
| 𖨆 | 1 |
| Distinct | 1 |
|---|---|
| Distinct (%) | < 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 17.0 KiB |
| elonmusk |
|---|
Length
| Max length | 8 |
|---|---|
| Median length | 8 |
| Mean length | 8 |
| Min length | 8 |
Characters and Unicode
| Total characters | 17256 |
|---|---|
| Distinct characters | 8 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | elonmusk |
|---|---|
| 2nd row | elonmusk |
| 3rd row | elonmusk |
| 4th row | elonmusk |
| 5th row | elonmusk |
Common Values
| Value | Count | Frequency (%) |
| elonmusk | 2157 |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| elonmusk | 2157 |
Most occurring characters
| Value | Count | Frequency (%) |
| e | 2157 | |
| l | 2157 | |
| o | 2157 | |
| n | 2157 | |
| m | 2157 | |
| u | 2157 | |
| s | 2157 | |
| k | 2157 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 17256 |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| e | 2157 | |
| l | 2157 | |
| o | 2157 | |
| n | 2157 | |
| m | 2157 | |
| u | 2157 | |
| s | 2157 | |
| k | 2157 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 17256 |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| e | 2157 | |
| l | 2157 | |
| o | 2157 | |
| n | 2157 | |
| m | 2157 | |
| u | 2157 | |
| s | 2157 | |
| k | 2157 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 17256 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| e | 2157 | |
| l | 2157 | |
| o | 2157 | |
| n | 2157 | |
| m | 2157 | |
| u | 2157 | |
| s | 2157 | |
| k | 2157 |
| Distinct | 7 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 0.1325915624 |
| Minimum | 0 |
|---|---|
| Maximum | 9 |
| Zeros | 1944 |
| Zeros (%) | 90.1% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 17.0 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 0 |
| median | 0 |
| Q3 | 0 |
| 95-th percentile | 1 |
| Maximum | 9 |
| Range | 9 |
| Interquartile range (IQR) | 0 |
Descriptive statistics
| Standard deviation | 0.4825713888 |
|---|---|
| Coefficient of variation (CV) | 3.639533166 |
| Kurtosis | 70.42842177 |
| Mean | 0.1325915624 |
| Median Absolute Deviation (MAD) | 0 |
| Skewness | 6.358881745 |
| Sum | 286 |
| Variance | 0.2328751453 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=7)
| Value | Count | Frequency (%) |
| 0 | 1944 | |
| 1 | 168 | 7.8% |
| 2 | 27 | 1.3% |
| 3 | 14 | 0.6% |
| 4 | 2 | 0.1% |
| 9 | 1 | < 0.1% |
| 5 | 1 | < 0.1% |
| Value | Count | Frequency (%) |
| 0 | 1944 | |
| 1 | 168 | 7.8% |
| 2 | 27 | 1.3% |
| 3 | 14 | 0.6% |
| 4 | 2 | 0.1% |
| 5 | 1 | < 0.1% |
| 9 | 1 | < 0.1% |
| Value | Count | Frequency (%) |
| 9 | 1 | < 0.1% |
| 5 | 1 | < 0.1% |
| 4 | 2 | 0.1% |
| 3 | 14 | 0.6% |
| 2 | 27 | 1.3% |
| 1 | 168 | 7.8% |
| 0 | 1944 |
| Distinct | 3 |
|---|---|
| Distinct (%) | 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 17.0 KiB |
| 0 | |
|---|---|
| 1 | 14 |
| 5 | 1 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 2157 |
|---|---|
| Distinct characters | 3 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 1 ? |
|---|---|
| Unique (%) | < 0.1% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 2142 | |
| 1 | 14 | 0.6% |
| 5 | 1 | < 0.1% |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| 0 | 2142 | |
| 1 | 14 | 0.6% |
| 5 | 1 | < 0.1% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 2142 | |
| 1 | 14 | 0.6% |
| 5 | 1 | < 0.1% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 2157 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 2142 | |
| 1 | 14 | 0.6% |
| 5 | 1 | < 0.1% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 2157 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 2142 | |
| 1 | 14 | 0.6% |
| 5 | 1 | < 0.1% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 2157 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 2142 | |
| 1 | 14 | 0.6% |
| 5 | 1 | < 0.1% |
| Distinct | 1 |
|---|---|
| Distinct (%) | < 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 17.0 KiB |
| 0 |
|---|
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 2157 |
|---|---|
| Distinct characters | 1 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 2157 |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| 0 | 2157 |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 2157 |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 2157 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 2157 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 2157 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 2157 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 2157 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 2157 |
| Distinct | 7 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 0.3092257765 |
| Minimum | 0 |
|---|---|
| Maximum | 6 |
| Zeros | 1668 |
| Zeros (%) | 77.3% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 17.0 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 0 |
| median | 0 |
| Q3 | 0 |
| 95-th percentile | 2 |
| Maximum | 6 |
| Range | 6 |
| Interquartile range (IQR) | 0 |
Descriptive statistics
| Standard deviation | 0.6612612362 |
|---|---|
| Coefficient of variation (CV) | 2.138441509 |
| Kurtosis | 9.471674108 |
| Mean | 0.3092257765 |
| Median Absolute Deviation (MAD) | 0 |
| Skewness | 2.704869448 |
| Sum | 667 |
| Variance | 0.4372664226 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=7)
| Value | Count | Frequency (%) |
| 0 | 1668 | |
| 1 | 357 | 16.6% |
| 2 | 99 | 4.6% |
| 3 | 23 | 1.1% |
| 4 | 8 | 0.4% |
| 5 | 1 | < 0.1% |
| 6 | 1 | < 0.1% |
| Value | Count | Frequency (%) |
| 0 | 1668 | |
| 1 | 357 | 16.6% |
| 2 | 99 | 4.6% |
| 3 | 23 | 1.1% |
| 4 | 8 | 0.4% |
| 5 | 1 | < 0.1% |
| 6 | 1 | < 0.1% |
| Value | Count | Frequency (%) |
| 6 | 1 | < 0.1% |
| 5 | 1 | < 0.1% |
| 4 | 8 | 0.4% |
| 3 | 23 | 1.1% |
| 2 | 99 | 4.6% |
| 1 | 357 | 16.6% |
| 0 | 1668 |
| Distinct | 8 |
|---|---|
| Distinct (%) | 0.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 0.3027352805 |
| Minimum | 0 |
|---|---|
| Maximum | 7 |
| Zeros | 1706 |
| Zeros (%) | 79.1% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 17.0 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 0 |
| median | 0 |
| Q3 | 0 |
| 95-th percentile | 2 |
| Maximum | 7 |
| Range | 7 |
| Interquartile range (IQR) | 0 |
Descriptive statistics
| Standard deviation | 0.7049105069 |
|---|---|
| Coefficient of variation (CV) | 2.328471613 |
| Kurtosis | 15.35249543 |
| Mean | 0.3027352805 |
| Median Absolute Deviation (MAD) | 0 |
| Skewness | 3.297873129 |
| Sum | 653 |
| Variance | 0.4968988227 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=8)
| Value | Count | Frequency (%) |
| 0 | 1706 | |
| 1 | 314 | 14.6% |
| 2 | 97 | 4.5% |
| 3 | 25 | 1.2% |
| 4 | 10 | 0.5% |
| 6 | 3 | 0.1% |
| 5 | 1 | < 0.1% |
| 7 | 1 | < 0.1% |
| Value | Count | Frequency (%) |
| 0 | 1706 | |
| 1 | 314 | 14.6% |
| 2 | 97 | 4.5% |
| 3 | 25 | 1.2% |
| 4 | 10 | 0.5% |
| 5 | 1 | < 0.1% |
| 6 | 3 | 0.1% |
| 7 | 1 | < 0.1% |
| Value | Count | Frequency (%) |
| 7 | 1 | < 0.1% |
| 6 | 3 | 0.1% |
| 5 | 1 | < 0.1% |
| 4 | 10 | 0.5% |
| 3 | 25 | 1.2% |
| 2 | 97 | 4.5% |
| 1 | 314 | 14.6% |
| 0 | 1706 |
| Distinct | 7 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 0.3597589244 |
| Minimum | 0 |
|---|---|
| Maximum | 6 |
| Zeros | 1609 |
| Zeros (%) | 74.6% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 17.0 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 0 |
| median | 0 |
| Q3 | 1 |
| 95-th percentile | 2 |
| Maximum | 6 |
| Range | 6 |
| Interquartile range (IQR) | 1 |
Descriptive statistics
| Standard deviation | 0.7356629268 |
|---|---|
| Coefficient of variation (CV) | 2.044877491 |
| Kurtosis | 10.41605446 |
| Mean | 0.3597589244 |
| Median Absolute Deviation (MAD) | 0 |
| Skewness | 2.768479621 |
| Sum | 776 |
| Variance | 0.5411999419 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=7)
| Value | Count | Frequency (%) |
| 0 | 1609 | |
| 1 | 391 | 18.1% |
| 2 | 110 | 5.1% |
| 3 | 32 | 1.5% |
| 4 | 9 | 0.4% |
| 6 | 3 | 0.1% |
| 5 | 3 | 0.1% |
| Value | Count | Frequency (%) |
| 0 | 1609 | |
| 1 | 391 | 18.1% |
| 2 | 110 | 5.1% |
| 3 | 32 | 1.5% |
| 4 | 9 | 0.4% |
| 5 | 3 | 0.1% |
| 6 | 3 | 0.1% |
| Value | Count | Frequency (%) |
| 6 | 3 | 0.1% |
| 5 | 3 | 0.1% |
| 4 | 9 | 0.4% |
| 3 | 32 | 1.5% |
| 2 | 110 | 5.1% |
| 1 | 391 | 18.1% |
| 0 | 1609 |
| Distinct | 1706 |
|---|---|
| Distinct (%) | 79.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 5082.331015 |
| Minimum | 6 |
|---|---|
| Maximum | 204414 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 17.0 KiB |
Quantile statistics
| Minimum | 6 |
|---|---|
| 5-th percentile | 67 |
| Q1 | 407 |
| median | 1312 |
| Q3 | 4015 |
| 95-th percentile | 22356.4 |
| Maximum | 204414 |
| Range | 204408 |
| Interquartile range (IQR) | 3608 |
Descriptive statistics
| Standard deviation | 12495.48218 |
|---|---|
| Coefficient of variation (CV) | 2.458612426 |
| Kurtosis | 62.6151601 |
| Mean | 5082.331015 |
| Median Absolute Deviation (MAD) | 1113 |
| Skewness | 6.500525765 |
| Sum | 10962588 |
| Variance | 156137075 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 130 | 6 | 0.3% |
| 50 | 5 | 0.2% |
| 422 | 5 | 0.2% |
| 158 | 5 | 0.2% |
| 444 | 5 | 0.2% |
| 47 | 5 | 0.2% |
| 26 | 5 | 0.2% |
| 67 | 5 | 0.2% |
| 354 | 4 | 0.2% |
| 80 | 4 | 0.2% |
| Other values (1696) | 2108 |
| Value | Count | Frequency (%) |
| 6 | 1 | < 0.1% |
| 9 | 1 | < 0.1% |
| 10 | 1 | < 0.1% |
| 11 | 1 | < 0.1% |
| 12 | 3 | |
| 13 | 2 | |
| 18 | 2 | |
| 19 | 1 | < 0.1% |
| 20 | 1 | < 0.1% |
| 21 | 2 |
| Value | Count | Frequency (%) |
| 204414 | 1 | |
| 151775 | 1 | |
| 144534 | 1 | |
| 117894 | 1 | |
| 111854 | 1 | |
| 104334 | 1 | |
| 102455 | 1 | |
| 89688 | 1 | |
| 88544 | 1 | |
| 86126 | 1 |
retweets_count
Real number (ℝ≥0)
HIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATIONHIGH CORRELATION| Distinct | 1870 |
|---|---|
| Distinct (%) | 86.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 13350.84979 |
| Minimum | 3 |
|---|---|
| Maximum | 582467 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 17.0 KiB |
Quantile statistics
| Minimum | 3 |
|---|---|
| 5-th percentile | 50 |
| Q1 | 605 |
| median | 3044 |
| Q3 | 12054 |
| 95-th percentile | 64299 |
| Maximum | 582467 |
| Range | 582464 |
| Interquartile range (IQR) | 11449 |
Descriptive statistics
| Standard deviation | 30914.36556 |
|---|---|
| Coefficient of variation (CV) | 2.315535419 |
| Kurtosis | 77.88037479 |
| Mean | 13350.84979 |
| Median Absolute Deviation (MAD) | 2835 |
| Skewness | 6.726566076 |
| Sum | 28797783 |
| Variance | 955697998.1 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 41 | 6 | 0.3% |
| 76 | 5 | 0.2% |
| 16 | 5 | 0.2% |
| 23 | 5 | 0.2% |
| 157 | 5 | 0.2% |
| 18 | 5 | 0.2% |
| 235 | 4 | 0.2% |
| 29 | 4 | 0.2% |
| 56 | 4 | 0.2% |
| 28 | 4 | 0.2% |
| Other values (1860) | 2110 |
| Value | Count | Frequency (%) |
| 3 | 2 | |
| 4 | 1 | < 0.1% |
| 6 | 1 | < 0.1% |
| 7 | 2 | |
| 8 | 2 | |
| 11 | 1 | < 0.1% |
| 12 | 1 | < 0.1% |
| 13 | 1 | < 0.1% |
| 14 | 4 | |
| 15 | 1 | < 0.1% |
| Value | Count | Frequency (%) |
| 582467 | 1 | |
| 377180 | 1 | |
| 295702 | 1 | |
| 279902 | 1 | |
| 275873 | 1 | |
| 238014 | 1 | |
| 219620 | 1 | |
| 218913 | 1 | |
| 202697 | 1 | |
| 198533 | 1 |
| Distinct | 2136 |
|---|---|
| Distinct (%) | 99.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 133849.3426 |
| Minimum | 95 |
|---|---|
| Maximum | 4727301 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 17.0 KiB |
Quantile statistics
| Minimum | 95 |
|---|---|
| 5-th percentile | 1099 |
| Q1 | 11054 |
| median | 41308 |
| Q3 | 138600 |
| 95-th percentile | 602881.4 |
| Maximum | 4727301 |
| Range | 4727206 |
| Interquartile range (IQR) | 127546 |
Descriptive statistics
| Standard deviation | 253783.9651 |
|---|---|
| Coefficient of variation (CV) | 1.896041924 |
| Kurtosis | 59.63098605 |
| Mean | 133849.3426 |
| Median Absolute Deviation (MAD) | 36566 |
| Skewness | 5.416606697 |
| Sum | 288713032 |
| Variance | 6.440630097 × 1010 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 1099 | 3 | 0.1% |
| 264 | 3 | 0.1% |
| 608 | 2 | 0.1% |
| 2893 | 2 | 0.1% |
| 2864 | 2 | 0.1% |
| 2398 | 2 | 0.1% |
| 9291 | 2 | 0.1% |
| 345 | 2 | 0.1% |
| 7999 | 2 | 0.1% |
| 405 | 2 | 0.1% |
| Other values (2126) | 2135 |
| Value | Count | Frequency (%) |
| 95 | 1 | |
| 126 | 1 | |
| 141 | 1 | |
| 147 | 1 | |
| 149 | 1 | |
| 150 | 1 | |
| 151 | 1 | |
| 160 | 1 | |
| 179 | 1 | |
| 181 | 1 |
| Value | Count | Frequency (%) |
| 4727301 | 1 | |
| 2040534 | 1 | |
| 1827250 | 1 | |
| 1716459 | 1 | |
| 1659146 | 1 | |
| 1626953 | 1 | |
| 1600607 | 1 | |
| 1582826 | 1 | |
| 1574723 | 1 | |
| 1571504 | 1 |
| Distinct | 37 |
|---|---|
| Distinct (%) | 1.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 5.184051924 |
| Minimum | 1 |
|---|---|
| Maximum | 47 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 17.0 KiB |
Quantile statistics
| Minimum | 1 |
|---|---|
| 5-th percentile | 1 |
| Q1 | 2 |
| median | 3 |
| Q3 | 7 |
| 95-th percentile | 15 |
| Maximum | 47 |
| Range | 46 |
| Interquartile range (IQR) | 5 |
Descriptive statistics
| Standard deviation | 5.304281683 |
|---|---|
| Coefficient of variation (CV) | 1.023192237 |
| Kurtosis | 8.729013126 |
| Mean | 5.184051924 |
| Median Absolute Deviation (MAD) | 2 |
| Skewness | 2.414540454 |
| Sum | 11182 |
| Variance | 28.13540417 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=37)
| Value | Count | Frequency (%) |
| 1 | 534 | |
| 2 | 339 | |
| 3 | 219 | |
| 4 | 200 | 9.3% |
| 5 | 163 | 7.6% |
| 6 | 137 | 6.4% |
| 7 | 95 | 4.4% |
| 8 | 69 | 3.2% |
| 9 | 61 | 2.8% |
| 11 | 57 | 2.6% |
| Other values (27) | 283 |
| Value | Count | Frequency (%) |
| 1 | 534 | |
| 2 | 339 | |
| 3 | 219 | |
| 4 | 200 | 9.3% |
| 5 | 163 | 7.6% |
| 6 | 137 | 6.4% |
| 7 | 95 | 4.4% |
| 8 | 69 | 3.2% |
| 9 | 61 | 2.8% |
| 10 | 51 | 2.4% |
| Value | Count | Frequency (%) |
| 47 | 1 | < 0.1% |
| 45 | 1 | < 0.1% |
| 42 | 1 | < 0.1% |
| 39 | 1 | < 0.1% |
| 35 | 1 | < 0.1% |
| 32 | 2 | |
| 31 | 3 | |
| 30 | 2 | |
| 29 | 2 | |
| 28 | 2 |
price
Real number (ℝ≥0)
| Distinct | 2116 |
|---|---|
| Distinct (%) | 98.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 201.011601 |
| Minimum | 35.79399872 |
|---|---|
| Maximum | 891.3800049 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 17.0 KiB |
Quantile statistics
| Minimum | 35.79399872 |
|---|---|
| 5-th percentile | 42.28680038 |
| Q1 | 56.59333293 |
| median | 68.78399658 |
| Q3 | 287 |
| 95-th percentile | 699.6060059 |
| Maximum | 891.3800049 |
| Range | 855.5860062 |
| Interquartile range (IQR) | 230.4066671 |
Descriptive statistics
| Standard deviation | 234.8994779 |
|---|---|
| Coefficient of variation (CV) | 1.168586672 |
| Kurtosis | 0.5824455492 |
| Mean | 201.011601 |
| Median Absolute Deviation (MAD) | 20.77399826 |
| Skewness | 1.43883195 |
| Sum | 433582.0234 |
| Variance | 55177.76472 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 855 | 3 | 0.1% |
| 72 | 3 | 0.1% |
| 59.34799957 | 2 | 0.1% |
| 60.79999924 | 2 | 0.1% |
| 55.31800079 | 2 | 0.1% |
| 65.55599976 | 2 | 0.1% |
| 69.98600006 | 2 | 0.1% |
| 63.22600174 | 2 | 0.1% |
| 63.20000076 | 2 | 0.1% |
| 62.2480011 | 2 | 0.1% |
| Other values (2106) | 2135 |
| Value | Count | Frequency (%) |
| 35.79399872 | 1 | |
| 36.22000122 | 1 | |
| 36.52999878 | 1 | |
| 36.55599976 | 1 | |
| 36.69800186 | 1 | |
| 36.7859993 | 1 | |
| 37.00400162 | 1 | |
| 37.02000046 | 1 | |
| 37.0320015 | 1 | |
| 37.04733404 | 1 |
| Value | Count | Frequency (%) |
| 891.3800049 | 1 | |
| 883.0900269 | 1 | |
| 880.0200195 | 1 | |
| 870.3499756 | 1 | |
| 869.6699829 | 1 | |
| 869.4133301 | 1 | |
| 861.4466553 | 1 | |
| 858.0266724 | 1 | |
| 857.0766805 | 1 | |
| 856 | 1 |
| Distinct | 2154 |
|---|---|
| Distinct (%) | 99.9% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 0.002025802196 |
| Minimum | -0.1184687551 |
|---|---|
| Maximum | 0.1715485869 |
| Zeros | 3 |
| Zeros (%) | 0.1% |
| Negative | 1008 |
| Negative (%) | 46.7% |
| Memory size | 17.0 KiB |
Quantile statistics
| Minimum | -0.1184687551 |
|---|---|
| 5-th percentile | -0.03610361542 |
| Q1 | -0.01005215046 |
| median | 0.001233700095 |
| Q3 | 0.01301956268 |
| 95-th percentile | 0.04023603586 |
| Maximum | 0.1715485869 |
| Range | 0.290017342 |
| Interquartile range (IQR) | 0.02307171313 |
Descriptive statistics
| Standard deviation | 0.02582046493 |
|---|---|
| Coefficient of variation (CV) | 12.74579768 |
| Kurtosis | 5.342198596 |
| Mean | 0.002025802196 |
| Median Absolute Deviation (MAD) | 0.01157895809 |
| Skewness | 0.7017843054 |
| Sum | 4.369655336 |
| Variance | 0.0006666964092 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 0 | 3 | 0.1% |
| 0.001619827649 | 2 | 0.1% |
| 0.002318119098 | 1 | < 0.1% |
| 0.01244815905 | 1 | < 0.1% |
| 0.0246936619 | 1 | < 0.1% |
| -0.02434964286 | 1 | < 0.1% |
| 0.02137085935 | 1 | < 0.1% |
| -0.01576337742 | 1 | < 0.1% |
| -0.007116599111 | 1 | < 0.1% |
| 0.03731796443 | 1 | < 0.1% |
| Other values (2144) | 2144 |
| Value | Count | Frequency (%) |
| -0.1184687551 | 1 | |
| -0.1075722805 | 1 | |
| -0.1025324905 | 1 | |
| -0.09879982736 | 1 | |
| -0.09761302495 | 1 | |
| -0.09576938373 | 1 | |
| -0.08972435472 | 1 | |
| -0.08572444839 | 1 | |
| -0.08572356895 | 1 | |
| -0.08410197571 | 1 |
| Value | Count | Frequency (%) |
| 0.1715485869 | 1 | |
| 0.1578024935 | 1 | |
| 0.1412863236 | 1 | |
| 0.1351372887 | 1 | |
| 0.1319999695 | 1 | |
| 0.1276189506 | 1 | |
| 0.1210861517 | 1 | |
| 0.1208025545 | 1 | |
| 0.1147155996 | 1 | |
| 0.1123035951 | 1 |
| Distinct | 3 |
|---|---|
| Distinct (%) | 0.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 2.4 KiB |
| no change | |
|---|---|
| rise | |
| drop |
Length
| Max length | 9 |
|---|---|
| Median length | 9 |
| Mean length | 7.993973111 |
| Min length | 4 |
Characters and Unicode
| Total characters | 17243 |
|---|---|
| Distinct characters | 13 |
| Distinct categories | 2 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | no change |
|---|---|
| 2nd row | no change |
| 3rd row | no change |
| 4th row | no change |
| 5th row | drop |
Common Values
| Value | Count | Frequency (%) |
| no change | 1723 | |
| rise | 249 | 11.5% |
| drop | 185 | 8.6% |
Length
Histogram of lengths of the category
Pie chart
| Value | Count | Frequency (%) |
| no | 1723 | |
| change | 1723 | |
| rise | 249 | 6.4% |
| drop | 185 | 4.8% |
Most occurring characters
| Value | Count | Frequency (%) |
| n | 3446 | |
| e | 1972 | |
| o | 1908 | |
| 1723 | ||
| c | 1723 | |
| h | 1723 | |
| a | 1723 | |
| g | 1723 | |
| r | 434 | 2.5% |
| i | 249 | 1.4% |
| Other values (3) | 619 | 3.6% |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 15520 | |
| Space Separator | 1723 | 10.0% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| n | 3446 | |
| e | 1972 | |
| o | 1908 | |
| c | 1723 | |
| h | 1723 | |
| a | 1723 | |
| g | 1723 | |
| r | 434 | 2.8% |
| i | 249 | 1.6% |
| s | 249 | 1.6% |
| Other values (2) | 370 | 2.4% |
Space Separator
| Value | Count | Frequency (%) |
| 1723 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 15520 | |
| Common | 1723 | 10.0% |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| n | 3446 | |
| e | 1972 | |
| o | 1908 | |
| c | 1723 | |
| h | 1723 | |
| a | 1723 | |
| g | 1723 | |
| r | 434 | 2.8% |
| i | 249 | 1.6% |
| s | 249 | 1.6% |
| Other values (2) | 370 | 2.4% |
Common
| Value | Count | Frequency (%) |
| 1723 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 17243 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| n | 3446 | |
| e | 1972 | |
| o | 1908 | |
| 1723 | ||
| c | 1723 | |
| h | 1723 | |
| a | 1723 | |
| g | 1723 | |
| r | 434 | 2.5% |
| i | 249 | 1.4% |
| Other values (3) | 619 | 3.6% |
Pearson's r
The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
Spearman's ρ
The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
Kendall's τ
Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
Phik (φk)
Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.Cramér's V (φc)
Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here. A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
First rows
| date | tweet | username | mentions | hashtags | cashtags | video | photos | urls | replies_count | retweets_count | likes_count | number of tweets | price | percent change | bins | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2016-08-23 16:00:00 | Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today | elonmusk | 0 | 0 | 0 | 0 | 0 | 0 | 872 | 4640 | 13965 | 2 | 44.967999 | 0.002318 | no change |
| 1 | 2016-08-28 09:30:00 | @Kotaku one of my favorite games as a kid @BelovedRevol Making progress. Maybe something to announce in a few months. Have played all prior Deus Ex. Not this one yet. | elonmusk | 0 | 0 | 0 | 0 | 0 | 0 | 68 | 137 | 789 | 2 | 44.162666 | 0.011081 | no change |
| 2 | 2016-08-30 16:00:00 | Thanks for the longstanding faith in SpaceX. We very much look forward to doing this milestone flight with you. https://t.co/U2UFez0OhY | elonmusk | 0 | 0 | 0 | 0 | 0 | 1 | 142 | 1839 | 7353 | 1 | 42.268002 | -0.022072 | no change |
| 3 | 2016-08-31 16:00:00 | @Lockyep Not allowed, according to HK regulations. Happy to do it if regs change. We need to do one more minor rev on 8.0 and then will go to wide release in a few weeks Writing post now with details. Will publish on Tesla website later today. Major improvements to Autopilot coming with V8.0 and 8.1 software (std OTA update) primarily through advanced processing of radar signals @newscientist uh oh | elonmusk | 0 | 0 | 0 | 0 | 0 | 0 | 391 | 2021 | 10165 | 5 | 42.402000 | 0.007508 | no change |
| 4 | 2016-09-01 16:00:00 | Loss of Falcon vehicle today during propellant fill operation. Originated around upper stage oxygen tank. Cause still unknown. More soon. | elonmusk | 0 | 0 | 0 | 0 | 0 | 0 | 1159 | 4848 | 10743 | 1 | 40.153999 | -0.039424 | drop |
| 5 | 2016-09-02 09:30:00 | @scrappydog yes. This seems instant from a human perspective, but it really a fast fire, not an explosion. Dragon would have been fine. Finishing Autopilot blog postponed to end of weekend | elonmusk | 0 | 0 | 0 | 0 | 0 | 0 | 426 | 588 | 3702 | 2 | 40.466000 | 0.007770 | no change |
| 6 | 2016-09-09 09:30:00 | Will get back to Autopilot update blog tomorrow. @ashwin7002 @NASA @faa @AFPAA We have not ruled that out. @LewisChandlerDN nope, it wasn't me Particularly trying to understand the quieter bang sound a few seconds before the fireball goes off. May come from rocket or something else. Support & advice from @NASA, @FAA, @AFPAA & others much appreciated. Please email any recordings of the event to report@spacex.com. Important to note that this happened during a routine filling operation. Engines were not on and there was no apparent heat source. Still working on the Falcon fireball investigation. Turning out to be the most difficult and complex failure we have ever had in 14 years. @waitbutwhy It's been a little crazy lately | elonmusk | 3 | 0 | 0 | 0 | 0 | 0 | 1761 | 5752 | 20453 | 8 | 39.818001 | 0.008766 | no change |
| 7 | 2016-09-09 16:00:00 | @abadcliche Most likely true, but we can't yet find it on any vehicle sensors | elonmusk | 1 | 0 | 0 | 0 | 0 | 0 | 31 | 23 | 189 | 1 | 38.894001 | -0.023206 | no change |
| 8 | 2016-09-10 09:30:00 | Thoughtful Op-ed in Space News much appreciated https://t.co/CJq5g3NIEK | elonmusk | 0 | 0 | 0 | 0 | 0 | 1 | 138 | 1400 | 3698 | 1 | 39.545334 | 0.016746 | no change |
| 9 | 2016-09-10 16:00:00 | Will do some press Q&A on Autopilot post at 11am PDT tmrw and then publish at noon. Sorry about delay. Unusually difficult couple of weeks. | elonmusk | 0 | 0 | 0 | 0 | 0 | 0 | 321 | 845 | 5376 | 1 | 39.149334 | -0.010014 | no change |
Last rows
| date | tweet | username | mentions | hashtags | cashtags | video | photos | urls | replies_count | retweets_count | likes_count | number of tweets | price | percent change | bins | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2147 | 2021-07-15 16:00:00 | @Erdayastronaut @Model3Owners How about a wifi camera link? @BLKMDL3 @Model3Owners In end, we kept production design almost exactly same as show car. Just some small tweaks here & there to make it slightly better. No door handles. Car recognizes you & opens door. Having all four wheels steer is amazing for nimble handling & tight turns! @johnkrausphotos @SpaceX @PortCanaveral Version 3 of the SpaceX droneship. Team did great work! Will be epic to see the deep sea oil rigs converted to ocean spaceports for Starship. @Model3Owners To be frank, there is always some chance that Cybertruck will flop, because it is so unlike anything else. I don’t care. I love it so much even if others don’t. Other trucks look like copies of the same thing, but Cybertruck looks like it was made by aliens from the future. @TesLatino @klwtts @jpr007 Tapering down charge rate is simply a physical thing that has to happen, as lithium ions bounce around what is an increasingly full “parking lot”. Just like a car parking lot, where it takes longer to find a spot when the lot is almost full. | elonmusk | 0 | 0 | 0 | 0 | 0 | 0 | 5420 | 4030 | 57028 | 5 | 650.599976 | -0.011832 | no change |
| 2148 | 2021-07-16 09:30:00 | @AustinTeslaClub @SpaceX @austinbarnard45 @PPathole @TeslaOwnersEBay @bluemoondance74 @teslaownersSV @JohnnaCrider1 @TeslaNY Absolutely! @Erdayastronaut @SpaceX Probably Monday @AaronS5_ @FrenchieEAP @karpathy Yes @FrenchieEAP @karpathy FSD beta 9 is using the pure vision production code for highway driving. Beta 10 hopefully (Beta 11 definitely) will use one stack to rule them all – city streets, highway & complex parking lots. | elonmusk | 0 | 0 | 0 | 0 | 0 | 0 | 2851 | 1771 | 40018 | 4 | 654.679993 | 0.006271 | no change |
| 2149 | 2021-07-16 16:00:00 | @Teslarati Improving permit approval speed & lowering permit costs for solar would make a big difference @TeslaNY Do you even press? @enn_nafnlaus @jpr007 @TesLatino @klwtts Indeed, but again like a parking lot, a battery having big “roads” tends to decrease number of “parking spaces” (ie stores less energy) @RationalEtienne @etherkragg Those are major factors | elonmusk | 0 | 0 | 0 | 0 | 0 | 0 | 2236 | 1346 | 25484 | 4 | 644.219971 | -0.015977 | no change |
| 2150 | 2021-07-17 09:30:00 | @techAU Roughly @JeffTutorials @TonyTesla4Life @WholeMarsBlog Yes @TonyTesla4Life @WholeMarsBlog Wide beta maybe with FSD rev 10, definitely with rev 11 @fael097 Pure coincidence! @ValaAfshar Even smaller to a @neuralink chip @facebookai To date, AI chatbots have had a rather short MtH (meantime to Hitler) score. Tay was ~16 hours. https://t.co/FnWMXgpZji @ErcXspace @NASASpaceflight @SpaceX Some of these design trades are still open, but will be resolved soon @ErcXspace @NASASpaceflight @SpaceX Very accurate! | elonmusk | 1 | 0 | 0 | 0 | 0 | 1 | 5382 | 3775 | 59586 | 8 | 646.416667 | 0.003410 | no change |
| 2151 | 2021-07-17 16:00:00 | @billycrammer @Tesla Cool! @engineers_feed There’s a corner case where brick density is same density as water, reaching bottom due to momentum Fred Astaire is incredible. Worth watching his movies. One of a kind. @TrungTPhan Now, he can bench press a rhino @SamTwits Nice Tap on the screen https://t.co/YPyyj8V8DF @AshleyIllusion1 @lexfridman Lil X is hodling his Doge like a champ. Literally never said the word “sell” even once! @lexfridman “All your basis points are belong to us” - fiat issuers @engineers_feed A classic @riorahardi618 True https://t.co/d4ZOSKZESP | elonmusk | 0 | 0 | 0 | 2 | 2 | 0 | 31886 | 30257 | 451470 | 11 | 644.886637 | -0.002367 | no change |
| 2152 | 2021-07-18 09:30:00 | Cybrrrtruck https://t.co/rdiMFdYOS6 @ArtifactsHub And all-time hodl champion @ValaAfshar Indeed @waitbutwhy Pohtaytohz @squawksquare Current Summon is sometimes useful, but mostly just a fun trick. Once we move summon (plus highway driving) to a single FSD stack, it will be sublime. | elonmusk | 0 | 0 | 0 | 1 | 0 | 0 | 18606 | 24221 | 291423 | 5 | 638.153341 | -0.010441 | no change |
| 2153 | 2021-07-18 16:00:00 | @thePiggsBoson Problem 1st, theory 2nd is for sure way to go, as it establishes relevance, thus improving memory retention | elonmusk | 0 | 0 | 0 | 0 | 0 | 0 | 475 | 252 | 2873 | 1 | 645.553304 | 0.011596 | no change |
| 2154 | 2021-07-19 09:30:00 | @DragTimes @Tesla Nice @WholeMarsBlog You don’t even need to touch the shifter in new S. Auto detect direction will come as an optional setting to all cars with FSD. | elonmusk | 0 | 0 | 0 | 0 | 0 | 0 | 1257 | 819 | 19337 | 2 | 629.890015 | -0.024263 | no change |
| 2155 | 2021-07-19 16:00:00 | @jack @BitcoinMagazine @CathieDWood Sure, I have a ton @BitcoinMagazine @jack @CathieDWood During this talk, we will sing a cover of The Final Countdown by Europe https://t.co/7YUXiW8dhd | elonmusk | 0 | 0 | 0 | 0 | 0 | 1 | 1953 | 1477 | 22616 | 2 | 646.219971 | 0.025925 | rise |
| 2156 | 2021-07-20 09:30:00 | @vincent13031925 Great to hear! @blueorigin Best of luck tomorrow! @TLPN_Official @SpaceX Depending on progress with Booster 4, we might try a 9 engine firing on Booster 3 Full test duration firing of 3 Raptors on Super Heavy Booster! | elonmusk | 0 | 0 | 0 | 0 | 0 | 0 | 7024 | 7245 | 145057 | 4 | 651.989990 | 0.008929 | no change |